RT Journal Article
T1 PlotToSat: A tool for generating time-series signatures from Sentinel-1 and Sentinel-2 at field-based plots for machine learning applications
A1 Miltiadou, Milto
A1 Grieve, Stuart
A1 Ruiz-Benito, Paloma
A1 Astigarraga, Julen
A1 Cruz Alonso, Verónica
A1 Triviño, Julián Tijerín
A1 Lines, Emily R.
AB PlotToSat offers a practical and time efficient way to the challenge of extracting time-series from multiple Earth Observation (EO) datasets at numerous plots spread across a landscape. This opens up new opportunities to understand and model various ecosystems. Regarding forest ecology, plot networks play a vital role in monitoring and understanding the dynamics of forest ecosystems. These networks often contain thousands of plots arranged systematically to represent an ecosystem. Combining field data collected at plots with EO time-series will allow us to better understand phenology and ecosystem composition, structure and distribution. Linking plot networks with EO data without PlotToSat is time consuming and computational expensive because plots are small and spread out, requiring data from multiple satellite tiles. PlotToSat processed a full year of multi-tile Sentinel-1 and Sentinel-2 data (estimated 18.3TB) at 15,962 plots from the fourth Spanish Forest Inventory in less than 24 h. PlotToSat, implemented using the Python API of Google Earth Engine, offers a new and unique workflow that is innovative due to its efficient, scalable and adaptable implementation. It supports Sentinel-1 and Sentinel-2 data, but its flexible design eases integration of additional EO datasets. New environmental modelling is expected to emerge facilitating EO time-series analyses and investigating interactive effects of environmental drivers.
PB Elsevier
SN 1364-8152
YR 2025
FD 2025-04-01
LK https://hdl.handle.net/20.500.14352/120847
UL https://hdl.handle.net/20.500.14352/120847
LA eng
NO Miltiadou, M., Grieve, S., Ruiz-Benito, P., Astigarraga, J., Cruz-Alonso, V., Tijerín Triviño, J., & Lines, E. R. (2025). PlotToSat: A tool for generating time-series signatures from Sentinel-1 and Sentinel-2 at field-based plots for machine learning applications. Environmental Modelling & Software, 188, 106395. https://doi.org/10.1016/j.envsoft.2025.106395
NO M. M., S. W. D. G. and E. R. L. were funded by a UKRI Future Leaders Fellowship (MR/T019832/1) awarded to E. R. L. and the University of Cambridge.M.M. was also partially funded by the Twinning Capability for the Natural Environment (TWINE) programme, which is co- delivered by the Met Office in partnership with the Natural Environment Research Council (NERC) and is part of Earth observation investment package (EOIP)..P. R-B. and J. A. acknowledge funding from the CLIMB-FOREST Horizon Europe Project (No 101059888) that was funded by the European UnionVCA was supported by the Ministry of Universities, Spain, and Next Generation-EU , with “Maria Zambrano” fellowship Spanish National Forest Inventory data were provided by the Spanish Ministry for the Ecological Transition and the Demographic challenge (MITECO).Special thanks are given to Amandine Debus and Eleanor Kent, who tested PloToSat before this paper was submitted for publication.
NO UK Research and Innovation (UKRI)
NO Unión Europea
NO Ministerio para la Transición Ecológica y el Reto Demográfico (España)
DS Docta Complutense
RD 18 mar 2026